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NOE ( Network Of Excellence ) Project
Project for Fostering and Promoting
Statistical Thinking
Project for Training Experts in
Statistical Sciences

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■ History——Message from Director-General

The Institute of Statistical Mathematics (ISM) was established in 1944 under the management of the Ministry of Education. Since then, ISM has been
dedicated to meeting the needs of the times and environment by developing
novel mathematical methods for data acquisition, interpretation, modeling,
and decision-making to support multiple disciplines. Statistical and mathematical sciences do not exist in a vacuum. The legacy of ISM is built upon the
belief that mathematical analysis of reality and facts paves the way to discovering the truth. Our primary mission in fundamental research is to continually create high-quality mathematical models that reconstruct reality andfacts.
It is also our mission to develop and apply powerful statistical and mathematical sciences methodologies and solve social challenges identified
through multidisciplinary or industry-government-academia collaborations.
Per the Fourth Medium-Term Plan, we are implementing international joint
programs using available resources efficiently while simultaneously pursuing
the Network Of Excellence (NOE) Project to expand joint activities in a top-down mannerand apply statistical and mathematical sciences to key research domains. The following
NOE-based activities are particularly noteworthy: (i) international collaborations led by
the Research Center for Statistical Machine Learning, (ii) “Creation of data infrastructure
for data-driven polymer materials research” project approved under the Program for Promoting Research on the Supercomputer Fugaku, and (iii) “New developments in space-timeearthquake forecasting and monitoring: from long-term to real-time” project, approved
under the Seismology TowArd Research innovation with data of Earthquake (STAR-E) Project. The second and third activities are represented by the Data Science Center for Creative
Design and Manufacturing and the Risk Analysis Research Center, respectively.
ISM’s further mission is to nurture specialists in statistical and mathematical sciences.
Compared to other countries, Japan has few postgraduate statistics courses. This has led
to a shortage of experts in this field. The national policy to foster specialists who can effectively utilize mathematics, data science, and artificial intelligence technologies underscores
the role of ISM. Along with the Project for Fostering and Promoting Statistical Thinking, theProject for Training Experts in Statistical Sciences, hosted by the Center for Training Professors in Statistics, encourages junior and mid-career researchers from 29 academic institutions to interact and collaborate with leading senior statistical scientists. This projectequips postgraduate statistics educators from various disciplines with the necessary educational tools and systems to incorporate statistics into their courses.
ISM’s Fourth Medium-Term Plan began in April 2022. More than three years have passed
since the sudden global outbreak of COVID-19. The pandemic imposed tremendous restrictions and changes to ISM’s activities. However, most COVID-19- related mandatory behavioral restrictions will be lifted in FY 2023. We are adapting to these changes as outlined in
our latest medium-term plan to facilitate the research and education activities of ISM, including resource adjustment and structural reorganization.
Finally, on behalf of ISM’s faculty and staff, I want to express our gratitude for the manyindustry, government, and academia stakeholders who support our statistical and mathematical sciences research. We thank you for your continued understanding and support.

Hiroe Tsubaki

Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on structural modeling of physical phenomena related to numerous factors,
and conducts research on model-based statistical inference methodologies. By means of model-based prediction and control,modeling of complex systems, and data assimilation, the department aims to contribute to the development of modeling intel­
ligence in many fields.

■ Prediction and Control Group ————————————————————————————————————————————The Prediction and Control Group works on the development and evaluation of statistical models, which function effectively
in terms of prediction and control of phenomena, decision making, and scientific discoveries. These efforts involve data
analysis and modeling related to phenomena that vary across time and space.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies aimed at discovering the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling. For these purposes, the group also considers
Monte Carlo simulations, discrete mathematics, and computer science.

■ Data Assimilation Group ———————————————————————————————————————————————The Data Assimilation Group works on the development of data assimilation techniques, which are procedures aimed
at combining information derived from large amounts of observations and a numerical simulation model. By developing
computational algorithms and high-performance parallel computing systems, the group aims to build a next-generation
simulation model that can predict the future in real time.

Department of Statistical Data Science
The Department of Statistical Data Science conducts research on data design methods aimed at managing uncertainty
and incompleteness of information, quantitative methods for evidence-based practice, and related data analysis methods.
Moreover, the department investigates methods for inferring the latent structures in target phenomena from observation data.

■ Survey Science Group ————————————————————————————————————————————————The Survey Science Group promotes research on the design of statistical surveys, development of statistical analysis methods on survey data, privacy protection in official statistics, and their applications. By exploring complex phenomena in various
fields, the group also aims to contribute to practical applications in academia and policy-making through social surveys.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group conducts research aimed at identifying and evaluating statistical evidence through quan­tification of phenomena that have not been measured thus far, as well as efficient information extraction from large
databases. The group investigates related methods and develops methods for analyzing the collected data. By working
on applied research in various fields of real science, the group aims to advance practical, applied, statistical mathematicalresearch based on evidence.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group conducts research on statistical science aimed at inferring the latent “structure” behindvarious target phenomena in biology, physics, and social science, based on observational data. The group focuseson machine learning, Bayesian reasoning, experimental design methods, and spatial-temporal analysis methods toinvestigate micro/meso/macroscopic and spatial-temporal dynamic structures in target phenomena.

Department of Statistical Inference and Mathematics
The Department of Statistical Inference and Mathematics carries out research into general statistical theory, statisticallearning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain
phenomena, stochastic processes and their application to inference, probability and distribution theory, and the related
mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data math­ematically and clarify the potential and the limitations of the data theoretically.

■ Mathematical Optimization Group —————————————————————————————————————————The Mathematical Optimization Group focuses on mathematical theory and practical applications of optimization andcomputational algorithms together with underlying numerical or functional analysis and discrete mathematics.

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NOE-type Research
Risk Analysis Research CenterWe are conducting research projects to scientifically mitigate the risks that modern society is facing. Our project activities include
the following: data analysis related to seismology, finance, environmental science and resource management; research on the theoreticaland practical aspects of analyzing spatiotemporal data; development of general statistical methods for risk analysis; and data collection
and linkage. Additionally, we operate a research network organization on risk analysis to facilitate interdisciplinary research with theshared objective of establishing a safe and resilient society.

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data. This field is basedon both the statistical science concerning inference from data and computer science concerning efficient algorithms. It can be applied
to broad disciplines ranging engineering and information science to natural science, such as robotics and brain sciences. Our research
center aims at supporting the academic community of this field, as well as producing influential research through various joint projects.

Data Science Center for Creative Design and Manufacturing
We aim to foster new scientific methods for innovative design and manufacturing. Various fields in manufacturing are now facing a
revolutionary period. Population reduction and globalization are bringing dramatic changes in the industrial structure in Japan.
Countries around the world has actively developed their growth strategies utilizing data science as a driving force. We have accumulated
state-of-the-art technologies in data science here. We are devoted to foster and practice advanced methods in data science for design
and manufacturing.

Research Center for Medical and Health Data Science
Research Center for Medical and Health Data Science aims to facilitate statistical data science research that covers medical studies,drug developments, health care, and public health. Our research projects involve fundamental mathematics and compu­tational science
for medical applications, applied methodology for basic, clinical and social medicine, and modern technology such as artificial
intelligence, machine learning, and big data analyses. Furthermore, our research center aims at constructing a research network of theacademic community of this field, as well as offering advanced statistical education programs.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking, from extension
courses to a professional development program. The researchers affiliated with the school are often involved with specific data analysis
projects, which help them to gain hands-on knowledge of data science. We expect such an experienced researcher will play an active role
as a number-cruncher, as a modeler, or as a project coordinator.

Center for Training Professors in Statistics
The Center has established the nationwide consortium of universities and promotes the Project for Training Experts in Statistical
Sciences to address the critical shortage of professors in statistics, which form the core of data science. Within 5 years from the startof the project, the Center will develop at least 30 university professors in statistics, who will train statistics experts at member
universities of the consortium. The Center aims to establish a positive cycle of professional development.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways: managing
computer systems and networks, editing and publishing journals, maintaining the library, and managing tutorial programs.

■ Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities and scientific software.
■ Computer Networking Unit The Computer Networking Unit is responsible for computer networking and its infra­structure, andnetwork security.
■ Information Resources Unit The Information Resources Unit is responsible for maintaining a library and an electronic repository,
and is in charge of planning statistical tutorial programs open to the public.
■ Media Development Unit The Media Development Unit is in charge of publishing and editing of research results and PR brochures.

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Research Topics
Risk Analysis Research Center

Bayesian Computation■ Bayesian inference■ Asymmetric MCMC method

Bayesian statistics is a framework of statistical inference that quantifies all uncertainty using probability distributions. Its consistency and clarity of conclusions are its
key features. However, its quantification often involves
complex computations. Appropriate statistical algorithms
are required depending on the situation.

One approach to avoid diffusive behaviour is to use amethod that does not maintain local balance. Local balance is not a necessary condition for accurate quantification. Various cases have shown that methods that do not
maintain local balance can improve convergence. However, designing an algorithm without local balance can bechallenging because global relationships are harder to capture than local ones. One approach to make the design easier is to extend the state space and add a twist to the extended space. Horowitz’s (1991) method, which added a
twist to the Hamiltonian Monte Carlo method, is a representative example.

■ Markov Chain Monte Carlo ( MCMC ) method
The Markov Chain Monte Carlo (MCMC) method is a
widely used algorithm that can calculate the characteristics of complex probability distributions, making it essential in Bayesian statistics. Most MCMC methods create astochastic flow that maintains local balance (reversibility),
which enables accurate quantification of uncertainty. However, due to the local balance, MCMC methods tend to be
diffusive, resulting in computational inefficiency.

■ Lifting
Another approach is lifting, which assigns a global positive or negative direction to the flow. By doing this, a chain
can go in one direction for a certain period of time. Although the local balance is not maintained due to the direction, two directions are taken with equal probability, resulting in overall global balance. Therefore, it is expected
to contribute to better convergence by reducing the possibility of being trapped.

■ Guided walk

Figure 1: Random walk Metropolis algorithm (Orange ), and Gustafson’s algorithm ( Blue).

Gustafson’s (1998) guided walk is a method that operates in a continuous state space (Figure 1). It is similar to
the most basic MCMC algorithm, the random walk Metropolis algorithm, but has a direction and moves a number oftimes to the positive or negative. This method is effective
in one dimension, but the positive and negative meanings
are unclear in higher dimensions, and significant improvements in convergence cannot always be expected. However, if a more meaningful direction, such as moving closer orfurther away from the center, is selected, we may expect
further improvements. We designed this scheme by using
the Haar measure (Figure 2). Additionally, we are studying
methods that use continuous-time Markov processes that
do not maintain local balance, rather than discrete-timeMarkov chains.

■ Conclusion
Recent advancements in MCMC algorithms have made it
possible to more practically apply statistical inference andmachine learning to complex problems. However, as new
types of datasets emerge, the design of these algorithms
needs to keep up, making it a challenging and active area of
research.

Kengo Kamatani

Figure 2: An MCMC with local balance (Orange ), and a new global balance method (Blue).

4Longitudinal Data Analysis■ Longitudinal data analysis
Data from multiple subjects, in which a response variable is measured repeatedly over time, are referred to as
longitudinal data. We have proposed and extended “autoregressive linear mixed effects models”, which combine
autoregressive models, which regress a response to a previous response, and linear mixed effects models. The model incorporates differences in responsiveness to interventions and the effects of the previous covariate history,
providing a unique variance-covariance structure. Similar
models have been developed independently in multiple
disciplines such as economics and sociology for different
purposes. As links between disciplines have been established, the interrelationships have become increasingly
clear. This will lead to further extensions and theoreticaldevelopments. We are also focusing on the interpretationof the model, such as unstable parameters showing shortterm phenomena and parameter transformations facilitat-

ing interpretation. We aim to develop methods to represent
the dynamics between variables measured over time.

■ Long-term indicatorsand prevention/countermeasures
We conduct research on phenomena with a long-time
lag between variables. For example, early life experience
may affect health in old age (Figure 1). There are growing
expectations for data science, such as data-driven, evidence-based, and big data, but if the time lag is very long,important data are often not observed. It is also not clear
whether the information from the observed generation canbe extrapolated/projected to (the future of) a new generation with a very different historical background. Ideally,
prevention/countermeasures should be available before
we see bad outcomes. The research aim is to leave a moreenriched society to the next generation and to develop amethodology for prevention/countermeasures.

■ Coronavirus disease 2019 (COVID-19)

Figure 1: Cumulative smoking initiation, smoking prevalence, andlung cancer mortality according to age (1925 birth cohorts). Funatogawa et al. 2013 modified. Long-time lag
between smoking initiation and lung cancer mortality.

Since the COVID-19 epidemic began, many researchersat the ISM have conducted various studies as countermeasures. A special issue of the Proceedings of the Institute of
Statistical Mathematics has also been published. As many
academic papers were published, some of them were difficult to interpret. The timing of infection is one such example, and a review paper on infectiousness was published.
Excess deaths are also becoming difficult to interpret. Excess deaths are the difference between observed and expected deaths, but the observed low deaths in 2020 in Japan might make subsequent expected deaths to be lower
than they should have been (Figure 2). Estimates of excess
mortality based on international data have been different
among leading research groups and some of the estimates
were in conflict with observed deaths. We need to ensure
robust indicators.

Ikuko Funatogawa

Figure 2: Weekly observed death 2000—2022 Japan ( left ) and weekly expected death 2017—2022 Japan ( right ).
Based on exdeaths-japan.org. Expected deaths had been rising, but expected deaths in 2021 were lower.

5Research Center for Statistical Machine Learning

Efficiency of the Invertible Function Estimation■ The rise of image generation and machinelearning technologies
By inputting few text words, desired images will be
generated instantly –– I am sure that many of you haveseen this technology at least once in social media
these days. Such a model, which does not necessarily
have to be limited to image generation, is called a “generative model,” and has been studied roughly over the
past 10 years.

■ The idea of a generative modelA brief description of the idea behind a generativemodel is shown in Fig. 1.

Figure 1: The idea behind a generative model.

Suppose we first learn the correspondence between
the data space and a latent space. Then, we obtainpoints in the latent space, say, corresponding points ofa triangle and a pentagon. If we take the average of thepoints obtained in the latent space, we can generate adata corresponding to the averaged point; we can expect to obtain the quadrangle whose property is also
in between. Although we considered triangles andpentagons as examples here for simplicity, if we takethe middle of the images of a black dog and a whitedog, we can expect to generate an image of a gray dog.
Applying this idea, we can generate any types of data
as long as we can collect sufficient training data.

the points in the latent space, and the function that
represents this one-to-one correspondence is specifically called an “invertible function”. Therefore, learning the generative model can be regarded as estimating the invertible functions, going back and forth
between data space and latent space.

■ Efficiency of the invertible function estimation
Since we have decided to use a special function
called an invertible function, we can expect to estimate the function more efficiently than the estimation
without any assumption. By mathematically evaluating how efficiently a function can be estimated, and bymathematically evaluating the limits of its efficiency,
we can determine which methods have a room for improvement when learning a generative model, andwhich methods have already achieved the maximum
efficiency. However, on the other hand, evaluating theestimation efficiency of invertible functions is difficult,and has been studied mainly only in the one-dimensional case, which is easy to prove.
In this research, in collaboration with Dr. Masaaki
Imaizumi of the University of Tokyo, we derived a theoretical limit (minimax rate) for the efficiency of estimating invertible functions between two-dimensional data
space and latent space, and proposed an estimator(Fig. 2) that achieves that limit. The paper is currently
under review and a preprint has been uploaded online*1.
The mathematical techniques used in this study canbe used for the general case of more than 2 dimensions.
We have already obtained partial results that extend
the two-dimensional results to three and more generaldimensions. We hope to publish these results in thenear future.

■ Invertible constraints in generative models
In the generative model described in Fig. 1, the correspondence between the data space and the latent
space is learned and used. If, for example, a triangle
corresponds to multiple points in the latent space, it
becomes ambiguous which point should be used fortaking the average. Therefore, the points in the data
space must have a one-to-one correspondence with6

Figure 2: An approximation used in the proposed estimator.
Reference: *1 https://arxiv.org/abs/2112.00213.

Akifumi Okuno

Research Topics

Privacy Protection of Machine Learning Models with Partially Synthetic Data
■ Machine Learning as a Service ( MLaaS)In recent years, cloud service providers such
as Google, Microsoft, and Amazon have provided customers with external APIs for machinelearning models. Such Machine Learning as aService (MLaaS) often employs machine learning models trained with personal data to support various tasks, including classification, prediction, and clustering. However, since the APIs
of MLaaS are exposed to the public, there exist
several possible attacks that cause the leakageof the training dataset of the model. In Figure 1,
an attacker could conduct model inversion attacks that restore information on sample data,
membership inference attacks that determines
whether a target sample x belongs to the trained
dataset D, or model extraction attacks thatsteals model parameters of the trained model.

Figure 1: Inference attacks on sensitive personal data in a training dataset.

■ Membership inference attacks
We mainly focus on membership inference
attacks that aim to infer membership, i.e., whether an individual belongs to a training dataset of
the target model. Although membership inference attacks seem to be much less ambitious
than model inversion attacks that restore sensitive attributes of samples, the risk of the forFigure 2: Process of constructing a model with a partially synthetic dataset.
mer is significant when all the individuals inthe dataset share the common sensitive attriba partially synthetic data. We combine records from anute, such as a medical database of a particular disease,original dataset with those from a synthetic data databecause the disclosure of the fact that an individual
set prepared with the post randomization method
belongs to the training set implies the leakage of the(PRAM) of flipping original record values with a given
individual’s sensitive attribute values. Also, previous
flipping rate p. By conducting experiments with theresearch shows that membership inference attacks exdataset of Purchase 100, we find that the practical stratploiting black-box access to a target model is feasible.
egy of turning two parameters for producing partially■ Membership inference countermeasure with
perturbed datasets; that is, we should first set the mixpartially synthetic data
ing ratio m of synthetic data high such that we can take
Existing countermeasures against membership inan adequate balance between the attack resistanceference attacks include differential privacy, confidence
and the model’s prediction accuracy by controlling thescore masking, and regularization. However, those
flip rate p of the post randomization method.
methods suffer either from insufficient resistance tothe attack or the degradation of model utility. We,
Reference: Wakana Maeda, Yuji Higuchi, Kazuhiro Minami, Ikuya
therefore, explore the approach of partially mixing
Morikawa. Membership Inference Countermeasure Withsynthetic data to the original data to achieve the moda Partially Synthetic Data Approach. The 4th International Conference on Data Intelligence and Security. August,el’s resistance to membership inference attacks while2022.
retaining the prediction accuracy of the trained model.
Kazuhiro Minami
Figure 2 shows the process of generating a model using
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Data Science Center for Creative Design and Manufacturing

Crystal Structure Prediction Using Surrogate Model Based Virtual Screening■ Crystal structure prediction problem
A crystalline solid is a state where molecules and atoms
are arranged and formed in infinite space with a certain
periodicity. Since crystal structure significantly impacts
the physical properties and functions of materials, crystal
structure prediction is considered a fundamental researchissue in academia and industrial needs.
The problem of crystal structure prediction (CSP) has
been studied for over 20 years. Crystal structure prediction by conventional experimental methods is time-consuming and costly. With the development of machine learning, a broad array of CSP methods has been developed todate. However, various issues still need to be solved, suchas the prediction accuracy of machine learning models andcomputational costs. There are strong expectations frommaterials exploration, drug design, and energy conversion
for solving crystal structure prediction problems. Against
this background, our group is collaborating with companies to develop a new method to solve the crystal structure
prediction problem fundamentally.

■ Crystal structure prediction using surrogate models
CSP aims to propose a stable crystal structure of a target compound based solely on its chemical formula. Thatmeans the crystal structures are deduced from the given
chemical formula, then evaluate the formation energy to
distinguish the stable structures. Structures with relatively
low energy are assumed to be stable. Therefore,
developing an efficient crystal structure generator and a method to evaluate the formation energy with high accuracy and speed are the keys
to solving the CSP problem. For crystal structure generation, we have trained models thatpredict information such as space groups, symmetry, and similarity of stable structures fromthe chemical formula and developed three
types of structure generators to consume thepredicted information. We trained the surrogate model to predict the formation energies ofgenerated structures and introduced a technique called
transfer learning to improve the accuracy of the model for
prediction on roughly generated structures.
We tested the performance of our approach on 35 selected benchmarks with diversity, complexity, and practical applicability kept in mind. Our proposed methods
achieve a minimum accuracy rate of 48.6% and a maximum
accuracy rate of 91.4%, compared to the accuracy rates ofmainstream structure prediction methods (e.g., USPEX).

■ Towards the discovery of innovative materials
Innovative materials only exist outside the periphery ofknown data. Although our approach has shown surprisingly high correctness on the existing data, “How usefulcan we be in discovering unknown substances?” the question remains unknown. Furthermore, if we can predict the
crystal structure, reasonable chemical formula, and synthesizability, and can build a system that seamlessly integrates all these predictors. We will surely bring great innovation into the fields such as functional materials andpharmaceuticals. In addition, analyzing information suchas crystallization conditions, polymorphism, and physical,
optical, and electromagnetic properties from newly discovered materials can provide various benefits for academics and industry. Therefore, polishing and automating
our approach will significantly increase the efficiency ofnovel materials discovery.

Chang Liu

Figure 2: Correctness between different methods on 35 benchmarks.

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Figure 1: A typical workflow of CSP.

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Research Topics

Creation of Polymer Properties Database Using Automated Molecular
Dynamics Simulation■ Understanding and discovery of thermallyconductive polymers
Organic polymers are materials with huge molecular weight, and have unique characteristics of lightness, ease of processing, and flexibility. On the other
hand, due to the vast parameter space of material design, it is required to establish efficient material exploration methods for further functionalization. The goalof our research is to discover polymeric materials with
innovative properties. Our group is participating in the
JST-CREST Nano-enabled Thermal Management from2019, and was granted for the MEXT “Program for Promoting Researches on the Supercomputer Fugaku” in
2021.

■ Machine learning technologies for automatedmolecular design
We applied a machine learning to the polymer database PoLyInfo to design high thermal conductive polymers. For thermal conductivity of amorphous polymers, only 28 samples were recorded at the database.
To overcome the difficulty of the limited data, we applied a transfer learning to obtain a predictive model ofthermal conductivity. By solving its inverse problem,
we created a virtual library of 1,000 candidate polymers with high thermal conductivity. From this library,
we synthesized three new polymers with a thermal
conductivity of 0.41 W/mK. This correspoznds to an improvement of 80% compared to commonly used amorphous polymers.

■ Lack of open data for data-driven research
The amount of data in materials research is muchless than in other applied fields of data science. There
are three main reasons for this: (1) the high cost of data
production; (2) the difficulty of creating common data
due to the diversity of researchers' needs; and (3) the
prevention of information leakage to competitors. In
particular, PoLyInfo is the only polymer database thatcan be used in data-driven research.

■ Creation of polymer property database with
automated molecular dynamics simulation
We developed a high-throughput calculation system for automated molecular dynamics simulation ofpolymer properties. We organize an industry-academia consortium with ISM, three universities, and 25 companies and we are creating a polymer properties database using this automatic system. Our ultimate goal is
to build a database that covers various properties formore than 100,000 polymers. For example, we will beable to comprehensively study polymer structures located at the Pareto boundaries between properties, andmay discover unique molecular skeletons that crossthe Pareto boundaries. We will accomplish this mission
as a joint research project under industry-academia
partnerships.
Yoshihiro Hayashi

Figure: Automated calculation of polymer properties using molecular dynamics simulation.

9

Research Center for Medical and Health Data Science

Pursuing the Advancement of Medical and Health Data Science■ Medical and health data science and thecenter’s mission
In recent years, expectations regarding data science
have been growing in various fields, including medical andhealth sciences. These days, the application of deep learning and other cutting-edge AI technologies to the analysis
of molecular/medical big data is widely expected to sharply
accelerate the elucidation of biological and disease mechanisms, the development of medical technologies, including
drug discovery, and the realization of precision medicine.
In the meantime, we should not forget the critical role ofobtaining solid evidence on medical technology from highquality small data derived through careful study designand statistical inference. While it is considered that expectations regarding medical and health data science will continue to grow, the framework of data science has yet to beestablished to fully meet such expectations; hence there isa big gap to be filled. That is to say, the enhancement ofeducation and research in medical and health data scienceis our great challenge for the future.

The Research Center for Medical and Health Data Science was established in April 2018 based on research andresearcher education in data science and on the network ofJapanese and foreign researchers that the Institute of Statistical Mathematics has established over many years. The
Center’s mission is to promote projects that enhance education and research in medical and health data science inJapan. The Center has been in existence for five years, it
has been involved in a variety of educational and research
activities thus far.

■ Educational and research activities
In the area of education, the Center has been promoting
the development of various educational programs, such assystematic education courses on theories and methods ofstatistical mathematics, biostatistics, theoretical epidemiology, and machine learning, topics that form the basis of
medical and health data science. In addition, the Centerhas focused on open lectures, as well as the developmentof e-learning materials to be shared with the research community.
In the research sector, the Center has been actively
working on a variety of projects, such as those involving
statistical methodologies pertaining to advances in medical technology and healthcare, as well as research in public health and social medicine. Additionally, the Center isinvolved in big data analysis using cutting-edge machinelearning and AI algorithms, and is performing studies onfoundational mathematics and computer technologies.

■ Medical and Health Data Science
Research Network
e-learning website.

All of the above projects are linked with the activities of
the newly launched “Medical and Health Data Science Research Network.” This unique network
comprises 100 organizations (as of February 2023), including related academic societies, universities, and research
institutions in all parts of Japan, as well
as hospitals and companies. The substantial advancement of medical andhealth data science depends on the organic collaboration of researchers in
the fields of statistical mathematics andinformation science and those in thefields of medical and health science.
The Center intends to make every possible effort to play an important part inbridging these fields.

The 15th Oxford Conference on Modelling and Control of Breathing ( 2022.10).

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Shigeyuki Matsui

Research Topics

Increased Suicide Rates in COVID-19 Outbreak— Understanding Regional and Gender Differences■ Suicide rate change during the corona disasterSuicide in Japan, which had been decreasing for 11
years since 2009, began to increase in 2020. The increase in the suicide rate among women was particularly pronounced. In this study, in order to understandregional and gender differences in the rise in the suicide rate in the corona epidemic, we created an index,“degree of rise in suicide rate,” which represents thehighs and lows of the rise in the suicide rate. We do notdiscuss the formula here, but it is a value that indicates
how large/small the increase in the suicide rate was foreach municipality relative to the degree of increase in
the national suicide rate after the corona. Panel data
were constructed for 1,735 municipalities.

■ Industrial structure and rising suicide rates

the suicide rate among women were concentrated in
the Izu Peninsula, which has a high employment rate inaccommodation and food services.

■ Why did the suicide rate among women rise?
The accommodation and food services was one of
the hardest hit industries by the Corona disaster. This
industry is characterized by a high percentage of women in the workforce and a prominently high percentage
of non-regular employees among the 14 major industries. It is highly likely that female employees were thefirst to be targeted in workforce reductions to avoid
bankruptcy, suggesting that this industrial structureand employment pattern may have been more strongly
affected by the economic crisis and increased the riskof suicide among women.
Mayumi Oka

The official statistics and data on 14 major industries were concatenated with the aforementioned panel
data. The results showed a significant positive correlation between
the suicide rate increase and theemployment rate in the domestic
demand-oriented service industry.
Only the employment rate for males
was significantly higher in the manufacturing industry, while the rate
for females was higher in the accommodation and food services industry. When the municipalities with
Figure 1: Rise in Suicide Rates in the Top 100 Municipalities with Employment in the Manufacturing and accommodation and food services.
the top 100 employment rates inthese industries were selected foranalysis, it became clear that thevariation in the degree of increasein the suicide rate for women in relation to the accommodation andfood services industry was verylarge (Figure 1).
The relationship between theemployment rate by industry andthe increase in the suicide rate in 35
municipalities in Shizuoka Prefecture was analyzed and mapped (Figure 2). Eight of the 11 municipalities
with particularly high increases in
Figure 2: Accommodation and Food Service Employment Rates
and Rising Suicide Rates in Shizuoka Prefecture.

11

Research Topics
Next-Generation Simulation NOE

A Novel Phylogenetic Method for a Macroevolutionary Modelof Phenotypic Traits.
■ Significance of trait data
In evolutionary biology, much effort is paid to understanding why the organism obtained its current
forms and how ancestral species have diverged. In
particular, molecular phylogenetic methods, which infer past phylogenetic trees from the current molecularsequence data such as DNA, originated in the Instituteof Statistical Mathematics and now are applied to various research projects. But, effective methodologies
are also desired for a trait. These kinds of methodologies are called “phylogenetic comparative methods”(PCMs). PCMs are suited to study which factors drive a
specific trait (e.g. the size or shape of a particular organ, or behavior data) and to adjust autocorrelation due
to the shared evolutionary history in a multi-species
regression analysis.

■ A dilemma of PCMs
Previous PCMs, however, have faced a dilemma between mathematically tractable but restrictive approaches (Gaussian-Process models) and flexible but
intractable approaches (a simulation-based processmodel of phenotype evolution built on population genetics frameworks). Gaussian Process PCMs are fasterand more efficient. But, its mathematical structure

sometimes requires unrealistic biological assumptions, which render the interpretation of estimated parameters difficult. A simulation-based PCMs, in contrast, allow more flexible and realistic modeling whichbrings interpretable parameters. But, its exact likelihood function is not known and thus demands quite aslower computational algorithm called approximate
Bayesian computation (ABC). Its statistical properties(e.g. bias, and Frequentist coverage properties of thecredible interval) were also questionable.

■ Proposed method
I proposed a novel branch-specific directional selection model (BSDS) to reconcile these two approaches (Ohkubo et al. 2023: Evolution). This methodology isbased on the fact that the previous simulation-basedmodel can be approximated up to the 2nd order andthat, though the full-likelihood function is not available,
a pseudo-likelihood function can be obtained without
any explicit simulations. The proposed method is fasterthan the previous method of ABC, as well as it achieves
better statistical performance. Extension to a multivariate trait model and the construction of a model selection procedure would be a future project.

Yusaku Ohkubo

Figure: An example of the empirical application of BSDS. We analyzed 13 species of apes to estimate the evolution of maximum waitingtime for delayed rewards.

12

Research Topics
Survey Science NOE“Giri ninjō” and “Kurashi-Kata” in the Heisei Era:
from the Surveys on the Japanese National Character■ The Japanese National Character in the Heisei era
The Institute of Statistical Mathematics (ISM) has conducted a nationwide survey every five years since 1953.
The 15 th nationwide survey will be conducted in the fall of2023; the 70th anniversary of the start of the survey. We
wanted to assess the values and attitudes of the Japanesepeople based on data from the 9th (1998) to 14th (2018)
waves of the Surveys on the Japanese National Characterso as to reflect the Japanese consciousness during theHeisei era, focusing on the survey’s long-term, continuous
items “Giri ninjō” and “kurashi-kata”.

■ “Giri ninjō” and “kurashi-kata” as the survey’s
long-term, continuous items
Looking at the responses to the “Girininjō” item, it was confirmed that “Girininjō” relationships were still favoured in
the Heisei period as you can see in fig.1.
The periods of the survey that reveal
similar distributions are (1998, 2003),(2008, 2013), and (2018), with 2008 and2013 showing close results to 1988. The
values that decreased from 1993 to 1998 in
the early Heisei period rose in the following three surveys starting in 2003. In other words, it was expected that the values
on the “Giri ninjō” scale in the Showa period would not be high and that the values
on the “Giri ninjō” scale in the Heisei period would continue to decrease. However,the 2008 and 2013 surveys show a returnto the high values of the 1988 level.
In addition, looking at the responses tothe “kurashi-kata” item, the most notabledecrease in Fig. 2 is observed for “resist allevils in the world and live a pure and justlife”. On the other hand, especially in theShowa period, two response options, “donot think about money or fame; just live alife that suits your taste” and “live eachday as it comes, cheerfully and withoutworrying”, increased. The former became
the most common opinion as early as
1958. These two options remained themost common during the Heisei period,
and this indicates that Japanese people in
the Heisei period placed importance on
enjoying the things around them and intheir daily lives.

Japan in the Heisei era was a time of reform and change
in all aspects of society, from the bursting of the economic
bubble to the global recession, including the collapse of
Lehman Brothers and the Great East Japan Earthquake. In
order to clarify the Sense of “Giri ninjō” and “Kurashi-Kata”
properly, it is necessary to consider expanding the model
to include the economic growth rate and other objective
variables while considering generational and historical factors. Furthermore, we are planning to conduct cross-cultural research on the national character including international comparison, so as to capture the Japanese National
Character over a wide range and in various contexts.

Yoosung Park

Figure 1: Variation in the values of the “Giri ninjō” scale during the Heisei era.

Figure 2: Variation in the distribution of answers to the “kurashi-kata” ( attitudes
towards life ) question from the first to the fourteenth survey.

13

NOE ( Network Of Excellence ) Project
For Promoting Interdisciplinary Research and for Solving Social
Problems with Industry-Academia-Government Collaborations■ Research and Educational Activities asa Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues research and education in the own biaxial structure; one is structured by three basic research departments and the other is by NOE-type research centersand organizations for professional development. By its
nature, the basic research departments cut across andlink various disciplines, with the goal of developing
tools for interdisciplinary research. The field of statistical mathematical sciences must itself evolve to meetthe changing needs of society and the data environment, and is therefore constantly evolving as a field ofstudy. At the same time, there are approaches and directions that have remained unchanged as the fieldevolves. There are three basic research departments:
Statistical Modeling, Statistical Data Science, and Statistical Inference and Mathematics. These departments
engage in cutting edge research aimed at developing
methodologies for rational prediction and decision
making, based on data and existing knowledge. We
regularly assess whether our research system is functioning effectively from the viewpoints of research
trends and prospects in statistical mathematical sciences. Basically, all faculty professors belong one ofthese three departments.
On the other hand, NOE-type research centers andorganizations for professional development are staffed

Fig. 1: Brief history of NOE Project.

14

by affiliated faculty professors, project professors/researchers (post-doctoral researchers), and visiting
professors and researchers. There are four NOE-typeresearch centers: the Risk Analysis Research Center,the Research Center for Statistical Machine Learning,
the Data Science Center for Creative Design and Manufacturing, and the Research Center for Medical and
Health Data Science. These centers conduct research
activities at the interface of statistical mathematicalsciences and individual scientific disciplines to find solutions to urgent social problems. Researchers are able
to join the activities in the center flexibly.
In addition, two organizations are committed to professional development. The School of Statistical Thinking, is the base for the Project for Fostering and Promoting Statistical Thinking and provides various programs.
The Center for Training Professors in Statistics is topromoting the Project for Training Experts in Statistical
Sciences. This center trains statistics professors andexperts in statistical sciences, primarily through graduate school education, and develop the statistical education system. For more details, please see each project’s page.

■ NOE ProjectIn accordance with the second medium-term planfor the Research Organization of Information and Systems (ROIS, ISM’s parent organization), ISM had set as

ISM Projects

a goal the establishment of NOE (Network Of Excellence) in statistical mathematics. Initially, ISM established NOE in Risk Research, Next-Generation Simulation, Survey Science, Statistical Machine Learning, andService Science. ISM reviews organizations of the project regularly, and ISM reorganized the NOE-type Research Centers in the past few years, considering theneeds of each community as well as modern society as
a whole (Fig.1). There are six NOE today in the fields of
Risk Research, Next-Generation Simulation, Survey
Science, Statistical Machine Learning, Data Science forCreative Design, and Medical and Health Data Science,
which promotes research and educational activities including cooperation with the Joint-Support Center for
Data Science Research, ROIS.
ISM has set the goal for this NOE Project which is importance to fulfill the goal of establishing new scientificmethodologies (“Fourth Paradigm: Data Science”) in aknowledge-based society, in which the importance ofknowledge goes beyond merely solving individual problems, the NOE activities are being systematically pursued under the unified guidelines formulated by theManaging Committee of the NOE Project. We also commission experts from the industrial, academic, and government sectors to be members of the Advisory Boardof the NOE Project, and their advice helps us to promote
the project much more effectively (Fig. 2).
The number of Memoranda of Understanding (MOUs)
with research organizations within Japan and overseas,
which are conducted by NOE-type research centers, isincreasing in each year. Particularly, academic exchang-

es encompassing multiple NOE domains provide significant opportunities to integrate different research fields
and create new ones. ISM provides flexibility for research and contributes to society. Consequently, ISM’s
general research in statistical mathematical sciences
is highly demanded by various research fields in boththe humanities and sciences. Recent reorganizations
at ISM have deepened and promoted the NOE Project.
In the fourth medium-term plan for six years of ROIS,
which began in April 2022 (and continues until March
2028), ISM set the goals; to promote the developmentof cutting-edge mathematical techniques, to promoteindustry-academia-government collaborations, to enhance relations with other research organizations andto increase NOE-type MOUs. For that, ISM will reorganize NOE-type research centers. Centers will be classified into methological and domain type. Members at
ISM have many discussions for much better researchand educational activities with the point of views in current and future, and ISM will reorganize both basic research departments and NOE-type research centers inthis fiscal year to accelerate cutting-edge research andcollaboration research.
ISM is continuously expanding and developing thisNOE project with the aim of establishing new scientificmethodologies, interdisciplinary research, creating
new research disciplines, industry-academia-government collaboration and so on. For up-to-date information, please visit the website. We truly appreciate yourunderstanding and support for this project.

https://www.ism.ac.jp/noe/project/en/

Fig. 2: Relationship Diagram of NOE Project.

15

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data. Now surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is a need for data
scientists who can handle such big data and are able to extract useful knowledge from it. Meanwhile, Japanesehigher education is exhibiting a deplorable lack of production capacity in terms of data scientists. This can be accounted for by the fact that, until quite recently, no academic institution other than ISM had a Ph. D. course in statistics and the small number of statisticians in academia are isolated from each other, being scattered over various disciplines. Hoping to gain a little traction on this problem, ISM established the School of Statistical Thinking,into which we integrated all of our educational resources. In FY 2016, ISM established the Managing Committeeof School of Statistical Thinking, inviting contributions from outside experts, and in FY 2017 we launched the
Leading DAT program by adopting the suggestions by the committee. In FY 2020, we launched online courses.
The following are the principal projects.

Research Collaboration Startup
The Institute had already been providing a consultation service for statistical science, but along withthe launch of the School of Statistical Thinking inNovember 2011, this service was reorganized as a
research collaboration startup. This program, being
one of the projects to foster and promote statisticalthinking, is mainly aimed at supporting applied scientists and other non-experts. Expert statisticians
affiliated with the Institute give them advice on statistical modeling, data analysis, and research. Somecases have developed into official research collaborations, which are our primary duty as an inter-university research institute. The Institute accepts around25 cases annually, some of which benefit society indiverse ways.

Data Science Research Plaza
Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees, and thecontract can be renewed annually. A faculty mentor
gives advice to the accepted funded researcher sothat he or she can freely attend various events, suchas seminars, workshops, conferences, and extension
courses. After learning the expertise of the ISM research staff, participants in this program are invited
to take advantage of paid consultations and funded
research collaboration.

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative research project. On the other hand, such goal setting
is irrelevant for a summer school program, study session, or retreat. Since the launch of the School of Statistical Thinking, organizers of such group-oriented
16

study programs can apply to the Open-type Professional Development Program. There are two categories under this program: one is ‘workshop’ and theother is ‘intensive training for young researchers’. For
FY 2022, nine workshops have been accepted after
review.

ISM Projects

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars
are led by in-house and external lecturers to showcase their latest studies. These lectures are currently
held online. To view the seminar schedule and learn
more about the program, please visit the Institute of
Statistical Mathematics website.
https://www.ism.ac.jp/index_e.html

Leading DATIn FY 2017, the School of Statistical Thinking
launched a program called “Leading DAT” aimed attraining data scientists with the knowledge and skills
in statistical mathematics required by modern society. In FY 2022, we held four Leading DAT
lectures and three Leading DAT free lectures online, entitled “L-A. Basics of Modern Statistics”, “L-B. An Introduction to
Statistical Modeling”, “L-S1. Basics andPractice of Decision Trees and Ensemble Learning”, “L-S2. Statistical Causal
Effect Estimation and its Applications”,
and “L-X1. Introduction to Graphical Models (1)-How to read and write graphs”,“L-X2. Introduction to Graphical Models(2)- Graphs and Probabilistic Inference”,“L-Y1. Introduction to Time Series Analy-

sis of Point Processes”. Additionally, lecture videos
and supporting videos for the first two days of the2021 L-A course are now available on YouTube freeof charge.

Online course ( L-S1 )Tutorial Courses
The education program at ISM dates back to 1944,
the year of founding. The Ministry of Education installed a training center within ISM to foster technicians in numerical computation. After the World War
II, this training center was relaunched in 1947 to develop pollsters and census takers. It helped to cultivate professionals in the field of statistical surveys,
while a growing number of entries from business andindustry coming for various types of training werealso observed.
Now the tutorial courses are operated by the

School of Statistical Thinking, which was establishedin 2011.
In FY 2022, 9 lectures were held and the number ofparticipants was 1,029. The total numbers of lectures and courses held from 1969 to March, 2023
were 401 and 5 respectively, with a total of 29,913
participants. These lectures covered a wide range offields from basic to applied statistics. The scheduleof tutorial courses can be found on the website of theInstitute of Statistical Mathe­matics.
https://www.ism.ac.jp/lectures/kouza.html
17

Project for Training Experts in Statistical Sciences

Many universities have recently estabConsortium
lished data science departments and faculParticipating organizations:Core organization: ISM
ties. This has led to a shortage of expert facUniversities, etc.
ulty members in statistics, which are theGraduate
Assistantprofessors,
schoolcore of data science. To address this issue,
3 terms of two-year
PDs, etc.
students
training sessions
the Institute of Statistical Mathematics (ISM)
Young researchers inStudents in master’svarious scientific fields
courses in various
The Program forhas launched the “Project for Training Exscientific fields
System for training
Training Professors
perts in Statistical Sciences” with the supexperts in statisticalin Statistics
sciences
port of the Ministry of Education, Culture,
Senior
About 500 experts
professors
in 10 years
Professors
Sports, Science and Technology (MEXT).
in statistics
Expert in
In this pioneering project, ISM trains
statisticalMore than 30
sciences
professors in 5 years
young researchers in various academic
fields at universities and research institutes
across Japan to become “professors in staProject framework.
tistics”. These professors in statistics will
give lectures on statistics, which is the foun■ Consortium for Training Experts indation of data analysis, to master students of graduStatistical Sciences
ate schools and supervise academic research using
statistics, which will develop “experts in statistical
In August 2021, ISM established the “Consortium
sciences” at universities and research institutes nafor Training Experts in Statistical Sciences” to promotetionwide. Then these experts will leverage statistics
the project in cooperation and collaboration withto contribute to academic research and industrial
universities and research institutes nationwide. The
promotion.
Consortium consists of the core organization, particiThe goal of the project is to establish a virtuous
pating organizations, and cooperating organizations.
cycle of professional development in the field of staAt the core of the Consortium is ISM. Universities
tistics. This project strives to develop at least 30 proand research institutes nationwide are either coopfessors in statistics over the 5-year project period
erating organizations if they participate in the develand to train approximately 500 experts in statisticalopment of education programs or participating orgasciences over a 10-year period, including the projectnizations. When the Consortium was established,period.
there were 21 participating organizations. Today there are 29 participating organizations.
In January 2022, ISM establishedthe “Center for Training Professors in
Statistics” as an official internal organization to ensure smooth operationof the Consortium. ISM also established a satellite at Shiga University asa base for training in western Japan.

■ Statistics Professors

Training Program

Members of Consortium for training experts in Statistical Sciences.

18

ISM provides the “Statistics Professors Training Program”, which is a
two-year program to train young researchers in various academic fields
at universities and research institutes
nationwide to become professors instatistics. The program is to support
three cohorts. The first cohort, whichstarted in October 2021, involved 6
senior professors training 12 young

ISM Projects

researchers. The second cohort, which started in April 2023, included 8 senior professors
training 13 young researchers. The third cohort should commence in April 2024.
The program aims to improve the trainees'
knowledge of statistics, strengthen their ability to give lectures on statistics, and enhancetheir skills to conduct academic research using statistics. To improve teaching abilities,
trainees repeatedly give “mock lectures” inwhich they take on the role of a teacher andlecture on various statistics topics to accumulate experience. Additionally, trainees attend
lectures in advanced data science areas suchas statistical causal inference and acquire
knowledge about programming languages such as
Python and R.
The goal of training is not to develop “statisticians” majoring in statistics. Instead, the program
strives to develop university faculty members who
can educate students on statistics and supervise academic research using statistics while respecting
the trainees' academic fields of specialization suchas engineering, medicine, pharmacy, economics, andliterature.

■ Workshop and Presentation ofTrainees’ Achievements

Each year, the Consortium holds a “Workshop forthe Development of Statistical Experts Training System”. Here, trends in statistics education in othercountries and the status of the development of statistics education programs at leading universities in Japan are shared to help universities and research institutes develop experts in statistical sciences. In
2022, the Consortium also hosted an invited lecture
series by prominent overseas researchers in the fieldsof statistics and data science education.
Trainees of the Statistics Professors Training Program present their research results based on theskills they have refined through the program at various academic conferences such as the Japanese Joint
Statistical Meeting organized by the Japanese Federation of Statistical Science Associations, and theInterim Professional Development Report Meetingorganized by ISM.

■ Self-assessments
In FY2022, which was the second year of the project, the Consortium conducted a self-assessment on
the progress and effectiveness of the efforts in theinitial phase of the project. For the self-assessment,

The Program for Training Professors in Statistics −Schedule.

ISM implemented a questionnaire survey of all consortium member organizations and trainees to obtain diverse perspectives and requests regarding theconsortium activities. In addition, a “Self-assessment
Advisory Board”, which consists of experts in statistics, was established to provide advice and recommendations. In February 2023, the Consortium released a “Self-assessment Report” on its website. In
response to issues identified in the assessment, theConsortium will improve its initiatives such as theStatistics Professors Training Program and consortium management.

■ Initiatives of Consortium Participating

Organizations

The actual situation of statistics education at consortium participating organizations is diverse. Somehave established graduate schools of data science,
while others have developed statistics education programs across graduate schools. Additionally, a few
organizations provide some statistics education ateach graduate school. Because the goal is for participating organizations to develop educational programs for the systematic study of statistics, various
efforts are being made. These include curricula reviews, the development of teaching materials, andprovision of computers and software necessary fordata analysis.
Graduate students who have completed a systematic course in statistics through this statistics education program are designated as Experts in Statistical
Sciences regardless of their specialty. Additionally,
graduate students who receive guidance directlyfrom professors in statistics who trained through theStatistics Professors Training Program are also designated as Experts in Statistical Sciences.

19

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes

20

Organization name

Address

Conclusion date

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washington )July 27, 1988

Stichting Mathematisch Centrum

The Kingdom of the Netherlands( Amsterdam )May 10, 1989

Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany ( Berlin )December 8, 2004

The Steklov Mathematical InstituteRussia ( Moscow )August 9, 2005

Central South University

China ( Changsha )November 18, 2005

Soongsil University

The Republic of Korea ( Seoul )April 27, 2006

University of Warwick

The United Kingdom ( Coventry )January 16, 2007

Indian Statistical Institute

India ( Kolkata )October 11, 2007

Institute of Statistical Science, Academia Sinica

Taiwan ( Taipei )June 19, 2008

Department of Empirical Inference, Max Planck Institute for Biological Cybernetics

Germany ( Tubingen )August 11, 2010

Department of Communication Systems,
SINTEF Information and Communication Technology

Norway ( Trondheim )January 30, 2012

University College London

The United Kingdom ( London )February 16, 2012

Department of Electronics and Telecommunications,
Norwegian University of Science and Technology

Norway ( Trondheim )May 22, 2012

Department of Probability and Mathematical Statistics, Charles University in Prague

Czech Republic (Prague )October 10, 2012

Department of Ecoinformatics, Biometrics and Forest Growth of the
Georg-August University of Goettingen

Germany (Goettingen )October 18, 2012

Korean Statistical Society (KSS )The Republic of Korea ( Seoul )July 9, 2013

Toyota Technological Institute at Chicago

USA ( Chicago )February 10, 2014

Australian National University

Australia ( Canberra )May 15, 2014

RiskLab ETH Zurich

Switzerland ( Zurich )February 7, 2015

Institut de Recherche en Composants logiciel et matériel pourl’Information et la Communication Avancee

France ( Paris )February 9, 2015

Centre de Rechereche en Informatique, Signal et Automatique de Lille

France ( Paris )February 12, 2015

University College London Big Data Institute

The United Kingdom ( London )February 26, 2015

The Institute of Forestry, Pokhara of Tribhuvan University

Nepal ( Pokhara )March 6, 2015

The Institute of Forest and Wildlife Research and Developmentof the Forestry Administration of Cambodia

Cambodia ( Phnom Penh )March 6, 2015

Forest Inventory and Planning Institute of Vietnam

Vietnam ( Hanoi )June 2, 2015

Zuse Institute Berlin

Germany (Berlin )June 20, 2016

The University of Porto

Portugal ( Porto )June 22, 2016

National University of Laos

Laos ( Vientiane )March 15, 2017

Institute of Geophysics China Earthquake Administration

China ( Beijing )April 28, 2017

Hong Kong Baptist University

Hong Kong ( Kowloon Tong )August 28, 2017

Unversidade de Évola

Portugal ( Evola )November 30, 2017

The Korean Association for Survey Research

The Republic of Korea ( Seoul )February 14, 2018

The Jean Golding Institute for data-intensive research, University of Bristol

The United Kingdom ( Bristol)January 15, 2019

Survey Research Center, Sungkyunkwan University

The Republic of Korea ( Seoul )February 25, 2019

University of LampungIndonesia ( Lampung )March 6, 2019

Department of Earth and Space Sciences,
Southern University of Science and Technology

China ( Shenzhen Shi )March 25, 2019

Université Bretagne Sud

France ( Lorient )March 29, 2019

North Carolina State University

USA ( Raleigh )November 13, 2019

Singapore-ETH Centre

Singapore

March 18, 2020

Department of Actuarial Studies and Business Analytics, Macquarie University

Australia (Sydney)

December 21, 2020

EURECOM

France (Sophia Antipolis)

August 25, 2021

International Cooperation
Associated Foreign Research Institutes
Organization name

Address

Conclusion date

The University of Texas at Dallas,School of Economic, Political and Policy Sciences

USA (Dallas )March 11, 2022

Universität Ulm

Germany ( Ulm )February 20, 2023

Laboratoire de Mathématiques Blaise Pascal, University of Clermont Auvergne

France ( Clermont-Ferrand )March 13, 2023

Istituto Nazionale di Oceanografia e di Geofisica Sperimentale ( OGS )Italy ( Sgonico )March 30, 2023

Research Collaboration
ISM performs many activities for collaborating with researchers in the various fields of statistical science, fromthe individual level to the national level. The ISM cooperative research program regularly performs research activities to provide the research resources of ISM to researchers at universities or research institutes in order toadvance their academic research. Available research resources include books, journals, supercomputers, somecommercial statistical software packages, as well as statistical packages developed by ISM, and also the researchers in ISM themselves, who have abundant professional knowledge and experience in statistical science and data
analysis. The ISM cooperative research program provides not only research support funds but also opportunities
for the various researchers in many fields who require statistical knowledge to make use of the resources available at ISM. ISM’s aim is to be a place for interaction and fusion among researchers inside and outside of ISM, andto contribute to multidisciplinary development of both the theory and the application of statistical science.

■ Number of Activities
Year

2017

2018

2019

2020

2021

2022

Number of Activities

161

166

178

145

143

122

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to find themost appropriate type of project.
ISM Fields
NumberFieldsa

Prediction and Control Groupb

Complex System ModelingGroup

Major Research Fields
NumberFieldsMajor Research Domains

1

Statisticalmathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

c

Data Assimilation Group3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

dSurvey Science Group4

Physical science

Space, planet, earth, polar region, materials, etc.

eMetric Science Group

5

Engineeringf

Structure Exploration Group

Mechanics, electronics, control, chemistry,architecture, etc.

g

Mathematical Statistics Group

6

Human science

Philosophy, art, psychology, education,
history, geography, culture, language, etc.

hLearning and Inference Group7

Social science

Economics, law, politics, society, manage­ment,
official statistics, population, etc.

i

Mathematical Optimization
Group
8

Environmental
science

Environmental statistics, environmentrics,
agricultural statistics, statistical meteorology,
land economics, landscape management,
forest management, etc.

9

OthersOther research fields

jOthers21

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (The Graduate University for Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has included the Department of Statistical Science and, since April 1989,
has accepted students for education and research in doctoral programs. In 2006, the Institute adopted a five-year
system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study. In April 2023, SOKENDAI has reorganized its educational structure andestablished the Statistical Science program under
Graduate Institute for Advanced Studies.

Outline of Education and Research
The Statistical Science program, which is based on the Institute of Statistical Mathematics (ISM) serving as its
underlying platform, aims to cultivate individuals who possess creative research skills to contribute to solving various important intricately-intertwined problems. To this end, the program conducts education and research related
to the basis, mathematics and applications of data collection designs, modeling, inference and prediction, and equip
students with the ability to extract information and knowledge from the real world based on the effective use of data.
Field of Education and Research

22

Contents

Statistical Modeling

We provide education and research focused on dynamic modeling such as spatial and space-time modeling, graphical modeling of temporally and/or spatially inter-related complex phenomena, and intelligent
information processing. We also provide education and research on statistical inference based on various
models, methods of calculation for inference, and evaluation of models based on data.

Statistical Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Statistical Inference
and Mathematics

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

Features of Education and Research● Statistical Science program is one of the few comprehensive doctoral programs in statistical science in Japan, andhas accepted students from a wide range of academic fields. Education and research in all aspects of statisticalscience is conducted by faculty members specializing in a variety of fields, from theory to application.
● The Institute of Statistical Mathematics, the platform for the program, is equipped with a world-class super computer, as well as a variety of software, including original statistical software developed by the Institute.
● The Institute of Statistical Mathematics has an extensive library covering a wide variety of journals and books onstatistical and mathematical sciences.
● The Institute of Statistical Mathematics, as a joint research institute, frequently holds research meetings andseminars presented by visiting professors and researchers from Japan and abroad. Students are encouraged toparticipate in these seminars and interact with the presenters.
● Students have the opportunity to participate in different research projects through collaborations with otheruniversities and research institutes.

Requirements for Graduation in/after AY 2023
■ 3-year doctoral program
To be enrolled at the Graduate Institute for Advanced Studies for more than three years (excluding the periodof leave of absence).
To earn at least 16 credits, including 12 credits of Dissertation Work in Advanced Studies ⅢA~ѴB.
■ 5-year doctoral program
To be enrolled at the Graduate Institute for Advanced Studies for more than five years (excluding the period ofleave of absence).
To earn at least 42 credits, including 20 credits of Dissertation Work in Advanced StudiesⅠA~ѴB.
To receive the necessary research guidance and pass an examination for a doctoral thesis.
To make full payment for the tuition fee (excluding students with the exemption of the tuition fee).

Number of Students ( As of April 1, 2023 )■ 5-year doctoral course:Quota,2■ 3-year doctoral course:Quota,3Year of enrollment

2019

2021

2022

2023

2016

2018

2019

2020

2021

2022

2023

Number of students

1

1

1

1

1①

3③

6⑤

3③

5③

9⑦

6④

* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 5 ) • Tohoku University (5 ) • Fukushima University (1 ) • University of Tsukuba (8) • Saitama University (1 )• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University (2 ) • Tokyo Institute of Technology ( 6 )• The University of Tokyo (30 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1) • Hitotsubashi
University (7) • Shizuoka University (1) • Kanazawa University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya
University (4 ) • Toyohashi University of Technology ( 2 ) • Kyoto University ( 9 ) • Osaka City University (1) • Osaka University ( 3 )• Nara Institute of Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 5 ) • Oita
University (1) • The University of Electro-Communications ( 2) • University College London (1 ) • University of London (1 )23Graduate School Program

University Background of Students
Private universities• Aoyama Gakuin University (1 ) • Kitasato University (1 ) • Keio University (8 ) • International Christian University (1) • Shibaura Institute ofTechnology (1 ) • Sophia University (1 ) • Chuo University (9 ) • Tokyo University of Science (7 ) • Toyo University (1) • Japan Women’s
University (1 ) • Nihon University ( 2 ) • Hosei University (7 ) • Waseda University (9 ) • Nanzan University (1 ) • Osaka Electro-Communication
University ( 1 ) • Kansai University ( 1 ) • Kyoto Sangyo University (1 ) • Ritsumeikan University (1 ) • Okayama University of Science (1 )• Kurume University (1 )Foreign universities• Aston University (1) • University of California, Irvine (1) • California State University, Long Beach (1) • University of Campinas (1)• University of Colorado Boulder (2) • University of Dhaka (2 ) • University of Hawaii (1) • Jahangirnagar University (2 ) • University of
Malaya (1) • Northeast Normal University (1) • Ohio University ( 2) • University of Rajshahi (2 ) • Stanford University (1) • The University of
Nottingham (1 ) • Zhejiang University (1 ) • Institute of Applied Mathematics, AMSS, CAS (1 ) • University of Science and Technology ofChina (1 ) • Center for Analysis and Prediction, China Seismological Bureau (1 ) • Northeastern University (1 ) • The Hong Kong Universityof Science and Technology (1 ) • China University of Geosciences (1 )Degrees AwardedYear
Doctor of Philosophy

2016

2017

2018

2019

2020

2021

2022

7

5

5

5

4

7

10

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute of Technology• University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University • Hokkaido
University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences • JAXA’s Engineering
Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan • Japan Broadcasting Corporation• Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis • Government Pension Investment Fund• Public School • RIKEN • Statistics Bureau of Japan • Pharmaceuticals and Medical Devices Agency ( PMDA ) • National Institute ofInformation and Communications Technology
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon University• Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Institute of Technology• Rikkyo University • Waseda University • Keio University • Tokyo Medical University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor Corporation,
Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating Organization of Japan• Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs Asset Management L.P. • CLC bio
Japan, Inc. • MUFG Bank, Ltd. • Pfizer Japan Inc. • Doctoral Institute for Evidence Based Policy • Sony Corporation • NTTIT Corporation• Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd.
• PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC
Clinical Trial Hospital, Medipolis Medical Research Institute • CRD Association • Japan Society for the Promotion of Science • Tokyo
Electric Power Company Holdings, Inc. • Asahi Kasei Corporation • Honda R&D Co.,Ltd. • Yokogawa Electric Corporation • Kao
Corporation • Advanced Smart Mobility Co., Ltd. • NEC Corporation • Janssen Pharmaceutical K.K. • Taisho Pharmaceutical Holdings• Otsuka Pharmaceutical Co., Ltd. • Kyowa Kirin Co., Ltd. • KOSÉ Corporation • Novartispharma K.K. • Nikkei Inc. • Mizuho–DL Financial
Technology Co., Ltd. • Daicel Corporation • Aichi Steel Corporation24

Facilities and Equipment

Computational Resources ( As of April 1, 2023 )ISM is operating a supercomputer system, the Supercomputer System for Statistical Science, which is suitable for analyzing large-scale datasets. The Supercomputer System for Statistical Science, an HPE SGI 8600 system,has been operated since October 2018. The system is a
distributed-memory parallel computer that has total theoretical peak performance of 1.49 PFLOPS. The system isliquid cooled and consists of 384 computing nodes. Eachnode has two CPU chips (Intel Xeon GOLD 6154) with 18
cores and has 384 GB memory. The system includes hardware random number generator.
In March 2021, we introduced the Communal Cloud
Computing System to provide a computing environmentthat is easy for each user to use and customize. This system is equipped with 64 computing nodes (HPE ProLiant
DL385 Gen 10 Plus; total theoretical computing performance of 154.0 TFLOPS), and each node has two 32-coreCPUs (AMD EPYC 7452), 1 TB of main memory, and an
SSD with 20 TB of usable capacity.
In March 2023, we launched the Supercomputer System for Data Assimilation to advance the analysis oflarge-scale data without parallel programming. This is a
distributed shared-memory computer with a large memory, which can be accessed from any CPU. This system
is equipped with two HPE Superdome Flex computing
nodes and has a total theoretical computing performanceof 154.8 TFLOPS. Each node is equipped with 32 28-coreCPUs (Intel Xeon Platinum 8280L) with a main memoryof 48 TB, and an SSD with 880 TB of usable capacity.

In the main office building, the primary local area
network (LAN) consists of an Ethernet network using
10GBase-SR for the main trunk and 1000Base-T forbranches. The personal computers in researchers’ offices and the supercomputer system are all connected tothis network. A wireless LAN system is also available in
the immediate area of the building occupied by ISM.
These LAN systems enable distributed processing andallow computing resources and statistical data to be
used effectively. Comprehensive network security methods have been implemented, such as a firewall system,anti-virus software, and an intrusion prevention system. To encourage joint research with researchers bothin Japan and abroad, as well as the exchange of e-mails,
the network is connected to the Internet through SINET6(100Gbps).

Supercomputer System for Data Assimilation ( HPE Superdome Flex )Library and Materials ( As of April 1, 2023 )We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.

25

Finance and Buildings

Administration Subsidy and Others ( 2022)
Type
Expenditure

Personnel expenses

Non-personnel expenses

Total

628,500

838,663

1,467,163
Unit: 1,000JPY

Accepted External Funds ( 2022)
Type

Joint research
Joint research division
Subcontracted research, Trustee businessContract researchers
Academic consulting
Contribution for scholarship
Total

Items

Income

20
2
23
—
7
2

32,72821,700515,557
—
6,424
4,100

54

580,509
Unit: 1,000JPY

Grants-in-Aid for Scientific Research “ KAKENHI” ( 2022)
Research Category

Grant-in-Aid for Scientific Research on Innovative Areas
Grant-in-Aid for Transformative Research Areas ( A )Grant-in-Aid for Transformative Research Areas ( B )Grant-in-Aid for Scientific Research ( S )Grant-in-Aid for Scientific Research ( A )Grant-in-Aid for Scientific Research ( B )Grant-in-Aid for Scientific Research (C)Grant-in-Aid for Challenging Research ( Exploratory)
Grant-in-Aid for Challenging Research ( Pioneering )Grant-in-Aid for Early-Career Scientists
Grant-in-Aid for Forming an Independent Foundation
Grant-in-Aid for Research Activity Start-up
Grant-in-Aid for JSPS Fellows
Total

Items

Amount Granted

2
1
1
—
2
9
14
1
1
14
—
3
—

19,37010,92010,140—15,86031,980
15,481
1,300
5,590
13,242
—
3,120
—

48

127,003
Unit: 1,000JPY

Site and Buildings ( As of April 1, 2023)

26

Site Area

62,450m 2

Area for Buildings ( total )16,209m 2

Organization

Organization Diagram ( As of April 1, 2023)

27

Number of Staff ( As of April 1, 2023 )DirectorGeneral

Type
Director-General

Assistant Administ­ Technical
Professor Associate
Professor Professor rative Staff
Staff

Total

1

1

Department of Statistical Modeling

7

3

10

Department of Statistical Data Science

7

6

2

15

Department of Statistical Inference and Mathematics

7

10

1

18

School of Statistical Thinking

55Center for Engineering and Technical Support

10

Administration Planning and Coordination Section
Tachikawa Administration Department
Total

1

21

19
(

8

10

1

1

( 39 )( 39 )

1 ( 39 )1060 ( 39 )) Total number of staff of Tachikawa Administration Department.

The number under Technical Staff at the Center for Engineering and Technical Support and Administrative staff at the Tachikawa Administration
Department include one each staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2023)
Director-General

Hiroe TSUBAKI

Vice Director-General

Yoshinori KAWASAKI

Vice Director-General

Satoshi YAMASHITA

Vice Director-General

Kazuhiro MINAMI

Department of Statistical Modeling
Director

Tomoko MATSUI• Prediction and Control Group ————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Yoshinori KAWASAKI
Fumikazu MIWAKEICHI

Prof.
Project Researcher

Jiancang ZHUANG
Hiroka HAMADA

Assoc. Prof.

Yumi TAKIZAWA• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.
Prof

Tomoko MATSUI
Kengo KAMATANIProf.
Assoc. Prof.

Yukito IBA
Shinsuke KOYAMAProf.

Hideitsu HINO

• Data Assimilation Group ———————————————————————————————————————————————————————————————
Shinya NAKANO
Zhiheng LIN
Visiting Prof.
Tomoyuki HIGUCHI
Visiting Assoc. Prof. Yosuke FUJII
Visiting Assoc. Prof. Hiromichi NAGAO

Genta UENO
Tadahiko SATO
Visiting Prof.
Masako KAMIYAMA
Visiting Assoc. Prof. Shunichi NOMURA
Visiting Assoc. Prof. Masaya SAITO

Shigeru FUJITA
Kazuyuki NAKAMURA
Visiting Prof.
Toshikazu KITANO
Visiting Assoc. Prof. Hiroshi KATO
Visiting Assoc. Prof. Takashi YAMAMOTO

Prof.

Prof.

Project. Prof.

Project Researcher

Visiting Prof.

Visiting Prof.

Department of Statistical Data Science
Director

Atsushi YOSHIMOTO• Survey Science Group —————————————————————————————————————————————————————————————————————
Kazuhiro MINAMI
Yoo Sung PARK
Project Assist. Prof. Mika ICHINO
Visiting Prof.
Toru KIKKAWA
Visiting Prof.
Wataru MATSUMOTO
Visiting Assoc. Prof. Yusuke INAGAKI

28

Atsushi YOSHIMOTO
Takao MURAKAMI
Project Researcher Yasuhiro TANAKA
Visiting Prof.
Takatoshi IMADA
Visiting Prof.
Kazufumi MANABE
Visiting Assoc. Prof. Taisuke FUJITA

Tadahiko MAEDA

Prof.

Prof.

Assoc. Prof.

Assoc. Prof.

Assoc. Prof.

Project Assist. Prof. Kiyohisa SHIBAI

Xiaoxing WANG
Yoshimichi SATO
Visiting Assoc. Prof. Naoko KATO
Visiting Assoc. Prof. Koken OZAKI
Project Researcher
Visiting Prof.

Organization

Staff
Department of Statistical Data Science• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.
Prof.
Assist. Prof.

Satoshi YAMASHITA
Hisashi NOMA
Nobuo SHIMIZU

Prof.
Prof.

Koji KANEFUJI
Takeshi EMURA

Prof.
Assoc. Prof.

Shigeyuki MATSUI
Ikuko FUNATOGAWA• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.
Visiting Prof.

Ryo YOSHIDA
Stephen WU
Tomoya MORI

Jun ADACHI
Daisuke MURAKAMI
Visiting Assoc. Prof. Yu OTAKEAssoc. Prof.

Assoc. Prof.

Assoc. Prof.

Assist. Prof.

Kenichiro SHIMATANI
Yoshihiro HAYASHI

Department of Statistical Inference and Mathematics
Director

Satoshi ITO• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.
Visiting Prof.

Satoshi KURIKI
Shogo KATO
Yoshihiko KONNO

Prof.
Assoc. Prof.

Yoshiyuki NINOMIYA
Takaaki SHIMURA

Prof.
Assoc. Prof.

Shuhei MANO
Keisuke YANO

• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Kenji FUKUMIZU
Masayuki HENMI

Prof.
Assoc. Prof.

Hironori FUJISAWA
Ayaka SAKATA

Assoc. Prof.
Assist. Prof.

Daichi MOCHIHASHI
LE Thanh Tam• Mathematical Optimization Group —————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Satoshi ITO
Bruno FIGUEIRA LOURENÇO

Prof.
Assoc. Prof.

Shiro IKEDA
Tasuku SOMA

Assoc. Prof.
Assoc. Prof.

Mirai TANAKALEE Ching-pei

Risk Analysis Research Center
Director

Satoshi YAMASHITA
Prof.
Tomoko MATSUIProf.
Yoshiyuki NINOMIYAProf.
Kengo KAMATANI
Assoc. Prof.
Keisuke YANO
Assoc. Prof.
Shogo KATO
Assoc. Prof.
Stephen WU
Project Assoc. Prof. Masayuki KUMON
Project Assist. Prof. XUE Yujie
Project Researcher PENG Hong
Visiting Prof.
Takashi KAMEYA
Visiting Prof.
Satoshi TAKIZAWA
Visiting Prof.
Tetsuji TONDA
Visiting Prof.
Katsutoshi NAGASHIMA
Visiting Prof.
Natsuki SANO
Visiting Prof.
Shinsuke ITO
Visiting Prof.
Sadaaki MIYAMOTO
Visiting Prof.
Yukihiko OKADA
Visiting Prof.
Isao TAKABE
Visiting Prof.
Satoshi FUJII
Visiting Prof.
Yasutaka SHIMIZU
Visiting Prof.
Makoto ITOH
Visiting Prof.
Hitoshi MOTOYAMA
Visiting Prof.
Masashi KONOSHIMA
Visiting Assoc. Prof. Dou XIAOLING
Prof.

Shogo KATO

Vice Director

Satoshi YAMASHITA

Satoshi KURIKI
Prof.
Atsushi YOSHIMOTO
Prof.
Kazuhiro MINAMIProf.
Jiancang ZHUANG
Assoc. Prof.
Masayuki HENMI
Assoc. Prof.
Yumi TAKIZAWA
Assoc. Prof.
Ikuko FUNATOGAWA
Project Assoc. Prof. Takao KUMAZAWA
Project Assist. Prof. Petrillo GIUSEPPE
Visiting Prof.
Yo SHEENA
Visiting Prof.
Shunji HASHIMOTO
Visiting Prof.
Mihoko MINAMI
Visiting Prof.
Rinya TAKAHASHI
Visiting Prof.
Tetsuya IWASA
Visiting Prof.
Takayuki SASAJIMA
Visiting Prof.
Takafumi KUBOTA
Visiting Prof.
Yasushi YOSHIDA
Visiting Prof.
Nakahiro YOSHIDA
Visiting Prof.
Tadashi ONO
Visiting Prof.
Kazuyuki SUZUKI
Visiting Prof.
Hideatsu TSUKAHARA
Visiting Prof.
Toshinao YOSHIBA
Visiting Prof.
Hiroshi MATSUZOE
Visiting Prof.
Hirokazu YANAGIHARA
Visiting Assoc. Prof. Teppei OGIHARA
Prof.

Koji KANEFUJI
Yoshinori KAWASAKIProf.
Shuhei MANO
Project Prof.
Kunio SHIMIZU
Assoc. Prof.
Kenichiro SHIMATANI
Assoc. Prof.
Takaaki SHIMURA
Assoc. Prof.
Daisuke MURAKAMI
Project Assist. Prof. TRAN Duc Vu
Project Researcher Yosihiko OGATA
Visiting Prof.
Masao UEKI
Visiting Prof.
Naoki SAKAI
Visiting Prof.
Toshihiro HORIGUCHI
Visiting Prof.
Yoshiki YAMAGATA
Visiting Prof.
Hiroaki NAGAFUJI
Visiting Prof.
Hisayuki HARA
Visiting Prof.
Kiyomi SHIRAKAWA
Visiting Prof.
Masakazu ANDO
Visiting Prof.
Takaaki YOSHINO
Visiting Prof.
Hiroshi TSUDA
Visiting Prof.
Masaaki FUKASAWA
Visiting Prof.
Toshio HONDA
Visiting Prof.
Aitaro KATO
Visiting Prof.
Kenichi KAMO
Visiting Prof.
Takenori TAKAHASHI
Visiting Assoc. Prof. Tomoaki IMOTO
Prof.
Prof.

29

Risk Analysis Research Center
Visiting Assoc. Prof. Koyomi NAKAZAWA

Visiting Assoc. Prof. Keisuke FUKUIVisiting Assoc. Prof. Noriyoshi SAKUMA

Visiting Assoc. Prof. Tatsuhiko SAIGOH

Visiting Assoc. Prof. Junichi TAKAHASHI

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Yuta KOIKE

Visiting Assoc. Prof. Hideaki NAGAHATA

Visiting Assoc. Prof. Yuta TANOUE

Visiting Assoc. Prof. Hayafumi WATANABE

Visiting Assoc. Prof. Yuuki RIKIMARU

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Kazuyoshi NANJO

Visiting Assoc. Prof. Masumi YAMADA

Visiting Assoc. Prof. Shunichi NOMURA

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Yuma UEHARA

Visiting Assoc. Prof. Yuta MITSUI

Research Center for Statistical Machine Learning
Director

Kenji FUKUMIZU

Kenji FUKUMIZU
Prof.
Satoshi ITO
Prof.
Shuhei MANO
Prof.
Hideitsu HINO
Assoc. Prof.
Ayaka SAKATA
Assoc. Prof.
LEE Ching-pei
Assist. Prof.
Akifumi OKUNO
Project Assist. Prof. Hideto NAKASHIMA
Visiting Prof.
Arthur GRETTON
Visiting Prof.
Masataka GOTO
Visiting Assoc. Prof. Masaaki IMAIZUMIVisiting Assoc. Prof. Tsutomu TAKEUCHI

Vice Director

Tomoko MATSUI

Tomoko MATSUIProf.
Shiro IKEDAProf.
Hironori FUJISAWA
Assoc. Prof.
Daichi MOCHIHASHI
Assoc. Prof.
Mirai TANAKA
Assist. Prof.
Kohei HATTORI
Assist. Prof.
LE Thanh Tam
Project Researcher ZHENG Ning
Visiting Prof.
Shuichi KAWANO
Visiting Prof.
Katsuki FUJISAWA
Visiting Assoc. Prof. Makoto YAMADA
Visiting Assoc. Prof. Sayaka SHIOTA

Prof.

Prof.

Yukito IBA
Satoshi KURIKI
Prof.
Kazuhiro MINAMI
Assoc. Prof.
Shinsuke KOYAMA
Assoc. Prof.
Daisuke MURAKAMI
Assist. Prof.
Masato SHIRASAKI
Project Assist. Prof. Kotaro SAKAMOTO
Project Researcher Syoji TOYOTA
Visiting Prof.
Konstantin MARKOV
Visiting Prof.
Yuji SHINANO
Visiting Assoc. Prof. Kei KOBAYASHI
Visiting Assoc. Prof. Eiji MOTOHASHI
Prof.
Prof.

Data Science Center for Creative Design and Manufacturing
Director

Ryo YOSHIDA
Hideitsu HINO
Assist. Prof.
Yoshihiro HAYASHI
Project Researcher Aiko TAKAHASHI
Project Researcher Syunya MINAMI
Project Researcher Kaoru KIMURA
Visiting Assoc. Prof. Masaaki TAKADA

Ryo YOSHIDA

Vice Director

Hironori FUJISAWA

Hironori FUJISAWA
Shinya NAKANO
Project Assist. Prof. Liu CHANG
Project Researcher Minoru KUSABA
Project Researcher Hironao YAMADA
Visiting Prof.
Junichiro SHIOMI

Kenji FUKUMIZU
Stephen WU
Project Assist. Prof. Keiko SHINODA
Project Researcher Yutaka MARUYAMA
Project Researcher Yoh NOGUCHI
Visiting Prof.
Junko MORIKAWA

Prof.

Prof.

Prof.

Prof.

Prof.

Assoc. Prof.

Research Center for Medical and Health Data Science
Director

Shigeyuki MATSUI

Satoshi YAMASHITA
Takeshi EMURA
Assoc. Prof.
Fumikazu MIWAKEICHI
Project Researcher Kousuke NAKAZONO
Visiting Prof.
Ken KIYONO
Visiting Prof.
Yoichi M. ITO
Visiting Prof.
Tosiya SATO
Visiting Prof.
Atsushi GOTO
Visiting Assoc. Prof. Chieko ISHIGURO
Visiting Assoc. Prof. Kazushi MARUO

Vice Director

Hisashi NOMA

Shigeyuki MATSUI
Shinto EGUCHI
Project Assoc. Prof. Mayumi OKA
Visiting Prof.
Manabu AKAZAWA
Visiting Prof.
Nobuaki NISHIYAMA
Visiting Prof.
Michiko WATANABE
Visiting Prof.
Satoshi HATTORI
Visiting Prof.
Masataka TAGURI
Visiting Assoc. Prof. Shonosuke SUGASAWA
Visiting Assoc. Prof. Haruhisa FUKUDA

Hisashi NOMA
Masayuki HENMI
Project Assoc. Prof. Koki MIMURA
Visiting Prof.
Senichiro KIKUCHI
Visiting Prof.
Hisateru TACHIMORI
Visiting Prof.
Kunihiko TAKAHASHI
Visiting Prof.
Satoshi TERAMUKAI
Visiting Assoc. Prof. Ryoichi KIMURA
Visiting Assoc. Prof. Yasunori SATO

Prof.

Prof.

Prof.

Prof.

Project Prof.

Assoc. Prof.

School of Statistical Thinking
Director
Prof.
Prof.

30

Yoshinori KAWASAKI
Hironori FUJISAWA

Satoshi KURIKI
Prof.
Prof.

Vice Director

Yukito IBA
Kazuhiro MINAMI

Yukito IBA
Prof.
Prof.

Satoshi KURIKI
Hideitsu HINO

Organization

Staff
School of Statistical Thinking

Shiro IKEDA
Kenichiro SHIMATANI
Assoc. Prof.
Ayaka SAKATA
Assist. Prof.
Masato SHIRASAKI
Assist. Prof.
Nobuo SHIMIZU
Visiting Assoc. Prof. Kei TAKAHASHI
Prof.

Project Prof.

Assoc. Prof.

Assoc. Prof.

Hiroko NAKANISHI
Masayuki HENMI
Akifumi OKUNO
Kei NOBA
Masayuki YOKOYAMA

Assist. Prof.
Assist. Prof.
Visiting Prof.

Kunio SHIMIZU
Keisuke YANO
Assist. Prof.
Kohei HATTORI
Assist. Prof.
Ryota YUASA
Visiting Assoc. Prof. Osamu KOMORI
Project Prof.
Assoc. Prof.

Center for Training Professors in Statistics
Masato CHINO

Director
Prof.
Project Prof.
Project Prof.
Project Prof.
Project Prof.
Project Prof.
Assist. Prof.

Yoshinori KAWASAKI
Masato CHINO
Manabu IWASAKI
Hideki ORIGASA
Kunio TANABE
Masahiro MIZUTA
Ryota YUASA

Satoshi YAMASHITA
Hiroko NAKANISHI
Yasunori SAWAMURA
Naoki KAMIYA
Kunio SHIMIZU
Shotaro AKAHOProf.
Project Prof.
Project Prof.
Project Prof.
Project Prof.
Project Prof.

Kazuhiro MINAMI
Kazuo MUROTA
Project Prof.
Naoto KUNITOMO
Project Prof.
Masakazu JIMBO
Project Prof.
Tetsuhisa MIWA
Project Assoc. Prof. Masayoshi TAKAYANAGIProf.

Project Prof.

Center for Engineering and Technical Support
Genta UENO
Fumikazu MIWAKEICHI

Director
Vice Director

Shinya NAKANOUnit Leader of Computing Facilities Unit
Unit Leader of Information Resources Unit

Vice Director

Mitsuru HAYASAKA
Noriaki MIYAZONO

Deputy Manager

Kazuhiro NAKAMURA
Kazuhiro NAKAMURA
Akiko NAGASHIMA

Unit Leader of Computer Networking Unit
Unit Leader of Media Development Unit

Library
Head

Genta UENO

Administration Planning and Coordination Section
Chief Director

Hiroe TSUBAKI
Director of Evaluation Unit

Vice Director of Evaluation Unit

Yoshinori KAWASAKI
Yoshihiko MIYASATO

Director of Information and Public Relations Unit

Yoshinori KAWASAKI
Kazuhiro MINAMI

Vice Director of Information and Public
Relations Unit

Yoshihiko MIYASATO

Director of Intellectual Property Unit

Satoshi YAMASHITA

Director of NOE Promotion Unit

Satoshi YAMASHITA

Director of Gender Equality Unit

Yoshinori KAWASAKI
Kazuhiro MINAMI

Director of Planning Unit

Director of International Affairs Unit

• URA Station ———————————————————————————————————————————————————————————————————————
Kozo KITAMURA

Motoi OKAMOTO

Keisuke HONDA

Tachikawa Administration Department
General Manager Tsutomu NUMATA• General Affairs Division

Manager Masaya MASADA

Head, General Affairs Team

Hisayuki IGARASHI

Head, Personnel and Labor Management
Team (ISM)

Yusuke KASAKAWA• Financial Division

ManagerHead, General Affairs and Audit Team

Takashi BAN

Deputy Manager

Deputy Manager

Hiroaki ARAI

Head, Budget and Account Settlement Team (ISM) Akiko MAEKAWA

Hisayuki IGARASHI

Head, Personnel and Labor Management
Team (NIPR)

Reiji YAMADA

Hiroaki ARAI

Head, Budget and Account Settlement Team (NIPR) Yuji HIRONAKA
Head, Assets Management and Acceptance Team

Taku SASAKI

31

StaffTachikawa Administration Department• Accounting Division

Manager

Takashi BAN

Deputy Manager

Junichi NAKATA
Hitoshi HIRAYAMA
Hiroshi TAKAGISpecialist
Head, Accounting Team
Head, Contract Team ( ISM )• Research Promotion Division

Manager• NIPR Planning and Coordination Division
Head, NIPR Planning and Coordination Team

Head, Contract Team ( NIPR )Head, Facilities Team

Deputy Manager

Hiromi OBAMA

Hiromi OBAMA

Head, Cooperative Research Team

Koji SAKAMOTO

Yuki SETOGUCHI• ISM Planning and Coordination Division
Head, ISM Planning and Coordination TeamManager

Kana KATO
Yumiko OKAWA
Takuya SAITO

Specialist

Masakazu ICHIKAWA

Isao SAKUMA

Head, Research Promotion Team

Hiroshi TAKAGIManager

Masaya MASADA

Deputy Manager

Wataru HASHIMOTO

Wataru HASHIMOTO

International Advisory Board ( As of April 1, 2023)
Song Xi CHEN

University Chair Professor, Peking University

Arthur GRETTON

Professor, University College London

Juan Carlos JIMÉNEZ-SOBRINO Professor, Departamento de Matemática Interdisciplinaria, Instituto de Cibernética, Matemática y Física
Jibum KIM

Professor, Sungkyunkwan University

Gareth W. PETERS

Chair Professor of Actuarial Science, Chair Professor of Statistics for Risk and Insurance,
Department of Statistics and Applied Probability, University of California Santa Barba

Evgeny SPODAREV

Professor, Ulm University

Council of The Institute of Statistical Mathematics ( As of April 1, 2023)

32

Keiko TAKAHASHI

Senior Researcher (Professor), Global Consolidated Research Institute for Science Wisdom,
Comprehensive Research Organization, Waseda University

Hiroshi MARUYAMAPFN Fellow, Preferred Networks, Inc.

Satoru IGUCHI

Professor, National Astronomical Observatory of Japan, National Institutes of Natural Sciences

Fumiyasu KOMAKI

Professor, Graduate School of Information Science and Technology, The University of Tokyo

You SHIINA

Professor, Dean of Faculty of Data Science, Shiga University

Hiroshi SAIGO

Professor, Faculty of Political Science and Economics, Waseda University

Yasuhiro OMORI

Professor, Faculty of Economics, University of Tokyo

Kenji KAJIWARA

Director, Institute of Mathematics for Industry, Kyushu University

Hideki ASOH

Adjunct Researcher, Artificial Intelligence Research Center, Department of Information Technology and Human Factors,
National Institute of Advanced Industrial Science and Technology

Mihoko MINAMI

Professor, Department of Mathematics, Faculty of Science and Technology, Keio University

Yoshinori KAWASAKI

Professor ( Vice Director-General, ISM )Satoshi YAMASHITA

Professor ( Vice Director-General, ISM )Kazuhiro MINAMI

Professor ( Vice Director-General, ISM )Tomoko MATSUI

Professor ( Director of Department of Statistical Modeling, ISM )Atsushi YOSHIMOTO

Professor ( Director of Department of Statistical Data Science, ISM )Satoshi ITOProfessor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM )Genta UENO

Professor ( Director of Center for Engineering and Technical Support, ISM )Satoshi KURIKI

Professor ( Director of School of Statistical Thinking, ISM )Kenji FUKUMIZU

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Hironori FUJISAWA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Organization

Cooperative Research Committee ( As of June 1, 2023)
Fumio ISHIOKA

Professor, Faculty of Environmental, Life, Natural
Science and Technology, Okayama University

Takafumi KUBOTA

Professor, School of Management &Information Sciences, Tama University

Shido SAI

Professor, Department of Economics, Facultyof Economics, Okayama Shoka University

Aki-Hiro SATO

Professor, Department of Data Science, Graduate
School of Data Science, Yokohama City University

Toshinao YOSHIBA

Professor, Graduate School ofManagement, Tokyo Metropolitan University

Kengo KAMATANI

Professor( Department of Statistical Modeling, ISM )Kenichiro SHIMATANI

Associate Professor( Department of Statistical Data Science, ISM )Mirai TANAKA

Associate Professor ( Department of Statistical
Inference and Mathematics, ISM )Hiroko NAKANISHI

Project Professor( Center for Training Professors in Statistics, ISM )Advisory Board of NOE Project ( As of April 1, 2023)
Satoshi ITOH

Chief Coordinator,
Foundation for Computational Science

Naonori UEDA

Deputy Director, RIKEN Center
for Advanced Intelligence Project ( AIP )Yasushi OKADA

President, The Japan Pharmaceutical
Manufacturers Association

Masafumi KAMACHI Director, Ocean Eyes Co., Ltd.

Yoshimichi SATO

Dean and Professor, Faculty of Humanitie,
Kyoto University of Advanced Science

Yutaka SOEJIMA

Yoshiki YAMAGATA

Professor, Graduate School of System Designand Management, Keio University

Director-General, Institute for Monetaryand Economic Studies, Bank of Japan

Managing Committee of School of Statistical Thinking ( As of April 1, 2023)
Ken KUROKAWA

Vice Director/Professor,
National Institute of Genetics

Manabu KOBAYASHI

Professor,
Center for Data Science, Waseda University

You SHIINA

Professor/Dean,
Faculty of Data Science, Shiga University

Yoshihiko NISHINO

Director, Advanced Analytics &AI Innovation Division, SAS Institute Japan, Ltd.

Jinfang WANG

Professor, School of International LiberalStudies, Waseda University

Satoshi KURIKI

Director ( School of Statistical Thinking, ISM )Yukito IBA

Vice Director ( School of Statistical Thinking, ISM )Yoshinori KAWASAKI

Professor ( Vice Director-General, ISM )Hiroko NAKANISHI

Project Professor( Center for Training Professors in Statistics, ISM )Research Ethics Board ( As of April 1, 2023)
Specialist on epidemiology and social research

Masayuki KANAI

Professor, School of Human Sciences, Senshu University

Specialist on epidemiology and social research

Keiko SATO

Associate Professor, Department of Health Informatics Graduate Schoolof Medicine & School of Public Health, Kyoto University

Specialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Specialist in the field of ethics and law

Hiroe TSUBAKI

Director-General, The Institute of Statistical Mathematics

Person in citizen’s position

Yutaka KURIKI

Director, Social welfare corporation Kunitachi nursery school

Research education staff of ISM

Hisashi NOMA

Professor ( Department of Data Science, ISM )Research education staff of ISM

Tadahiko MAEDA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Yoo Sung PARK

Associate Professor ( Department of Data Science, ISM )Professor Emeritus ( As of April 1, 2023)
Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

Hiroe TSUBAKI

Genshiro KITAGAWA

Nobuhisa KASHIWAGI

Takashi NAKAMURA

Yoshiyasu TAMURA

Tomoyuki HIGUCHI

Junji NAKANO

Shinto EGUCHI

Ryozo YOSHINO

Yoshihiko MIYASATO

33

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social
science theories ), and the 3rd Research Dept. ( operations research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation ofInformation and Systems of the Inter-University Research Institute based on the National University Corporation Law. The Planning Coordination Chief System was abolished and the position of Vice Director-General was
instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Scienceswere instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.

A new office building was constructed in Minato Ward.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section ( hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.
2009

34

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

2017

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.

January

Survey Science Center and Service Science Research Center were closed.

July

Data Science Center for Creative Design and Manufacturing was instituted.

December

The International Affairs Unit were instituted, and the Gender Equality Unit, which had been within the Planning
Unit, reorganized within APCS.
The Intellectual Property Unit was reorganized as the Industry-Academia Collaboration and Intellectual Property
Unit within APCS.

2018

April

Research Center for Medical and Health Data Science was instituted.
The NIPR/ISM Joint Administration Office was reorganized as the Tachikawa Administration Department of
the Research Organization of Information and Systems ( ROIS ).

2019

March

2021

April

2022

January

Research and Development Center for Data Assimilation was closed.
ISM Planning and Coordination Division was instituted within Tachikawa Administration Department.
Center for Training Professors in Statistics was instituted.

35